Accurate identification of the lung on chest images provides us not only with information for various lung-related computer-aided diagnosis schemes but also with a useful tool for anatomic region-based image processing and data compression. All edges provide useful information on the location, shape, and size of the lung fields. From this information CAD (Computer-Aided Diagnosis) systems can automatically detect heart's and lung's information and various abnormalities such as interstitial disease, pneumothorax, cardiomegaly and pulmonary nodules.
At present the usual steps to get borders of lung, heart and diaphragm include: get the landmark lines in anatomic sense by the gray level profile; find borders in ROI (Region of Interest) marked by the landmark lines by means of analysis of histogram, edge detection or some other methods based on the gray value difference between the borders and the background. The process is often done iteratively for the more accurate borders. The border will be smoothed by means of fitting functions or some other methods. There will be a lot of experienced data being used in the whole procedure.
U.S. Pat. No. 6,282,307 discloses a method, system, and computer product for the automated segmentation of the lung fields and costophrenic angle regions in posteroanterior chest radiographs wherein image segmentation based on gray-level threshold analysis is performed by applying an iterative global gray-level thresholding method to a chest image based on the features of a global gray-level histogram. Features of the regions in a binary image constructed at each iteration are identified and analyzed to exclude regions external to the lung fields. The initial lung contours that result from this global process are used to facilitate a local gray-level thresholding method. Individual regions-of-interest (ROIs) are placed along the initial contour. Contrast-based information is employed on a column-by-column basis to identify initial diaphragm points, and maximum gray-level information is used on a row-by-row basis to identify initial costal points. Analysis of initial diaphragm and costal points allows for appropriate adjustment of CP angle ROI positioning. Polynomial curve-fitting is used to combine the diaphragm and costal points into a continuous, smooth CP angle delineation. This delineation is then spliced into the final lung segmentation contours.
However, the prior art is complex in calculation, thus is slow in finding the borderlines.